AI for Natural Disaster Management

Summary

Dr. Monique Kudlish, Innovation Manager at the Fraunhofer Institute of Telecommunication and chair of the ITU-WMO Focus Group on AI for natural disaster management, discusses the potential of AI in mitigating natural disaster risks in Africa. She highlights the importance of leveraging satellite and ground data for early warnings and emphasizes the need for accessible information to prevent acute food insecurity from becoming chronic. The conversation also touches on the challenges of data accessibility and the role of AI in various stages of disaster management, including preparedness and response.

Takeaways

• 🌍 Natural disasters, such as floods, droughts, and storms, are particularly impactful in Africa due to their wide-ranging effects on health, property, and the environment.
• 📈 The loss of agricultural production due to natural disasters is estimated to affect 2-4% of Africa's GDP, highlighting the importance of effective disaster management.
• 🤖 AI can enhance the prediction and mitigation of natural disasters in Africa by leveraging satellite data and machine learning to provide early warnings and improve decision-making.
• 🌡️ Projects like NASA's Harvest program combine earth observations and weather forecasts with AI to offer early warnings, especially beneficial for smallholder farmers.
• 📊 The accessibility of data is crucial, but it often requires processing and understanding metadata to be effectively used for forecasting and disaster management.
• 🏞️ In Africa, the impact of natural disasters on health can be both direct, such as heat stroke or asphyxiation, and indirect, like waterborne illnesses or food shortages.
• 🔄 AI's role in disaster management includes all stages of the cycle, from preparedness and response to recovery, with a focus on data collection, modeling, and effective communication.
• 🌐 The ITU-WMO Focus Group on AI for Natural Disaster Management aims to create a roadmap, conduct workshops, and produce technical reports to enhance the use of AI in this field.
• 📱 The potential of smartphones in AI applications should not be underestimated, as they can be used for various tasks, including identifying pests or health issues in resource-limited settings.
• 🏛️ Governments and NGOs in Africa are encouraged to engage with the focus group to improve natural disaster preparedness and response, utilizing AI and data analysis.

Q & A

What is the role of Dr. Monique Kudlish in the field of AI for natural disaster management?

Dr. Monique Kudlish is the Innovation Manager at the Fraunhofer Institute of Telecommunication in Berlin, Germany, and she is also the chair of the newly established ITU-WMO Focus Group on AI for Natural Disaster Management.

How does Dr. Kudlish define natural disasters?

Natural disasters are defined as damaging physical events of a predominantly natural origin, which can include atmospheric, hydrologic, geophysical, oceanographic, and biologic events. They can lead to injury, mortality, displacement, property damage, and disturbance to natural resources.

Which natural disasters are most impactful in Africa according to the podcast?

On a continental scale in Africa, epidemics, floods, droughts, and storms are considered the most impactful natural disasters in terms of impacts and mortality.

How does climate change affect food and nutrition security in Africa?

Climate change affects food and nutrition security in Africa by impacting agricultural production, which is estimated to cause a loss between two and four percent of GDP.

How can AI help African farmers predict natural disasters and forecast agricultural production?

AI can leverage ground and remotely sensed data, such as satellite imagery, to provide insights into the mechanisms of natural disasters and improve forecasting and warning systems for such events, helping farmers make informed decisions and adaptation plans.

What is the NASA Harvest program and how does it assist African farmers?

NASA's Harvest program works with earth observations and weather forecasts combined with machine learning methods to provide early warnings, particularly for smallholder farmers in Africa, to help them prepare for natural disasters.

What challenges does Africa face in terms of accessing and utilizing AI for natural disaster management?

Africa faces challenges such as ensuring that information about natural disaster threats is accessible to all involved in the food system, including decision-makers, farmers, and traders. Additionally, the continent's diverse and small-scale farms require customized adaptation strategies.

How does the availability and accessibility of data from sources like NASA impact AI's role in natural disaster management?

Data from sources like NASA is publicly available but may require processing and understanding of metadata and data limitations before it can be effectively used in AI models for natural disaster management.

What types of data are useful for AI models in predicting and managing natural disasters?

Useful data for AI models includes satellite data, drone imagery, instrumental data from weather stations and river gauges, and crowdsourced data. These can be integrated into AI models to enhance prediction and management of natural disasters.

How does Dr. Kudlish see AI mitigating the risk associated with the effects of climate change in Africa?

Dr. Kudlish believes AI can provide insights into the mechanisms behind hydrometeorological hazards, help detect disasters, and offer more accurate forecasts, which can lead to more informed decisions and potentially reduce the costs associated with such events.

What is the ITU-WMO Focus Group on AI for Natural Disaster Management working on, and how can stakeholders get involved?

The ITU-WMO Focus Group is working on building a roadmap of AI activities, organizing workshops, creating technical reports, and developing educational materials. Stakeholders can get involved by participating in these activities and engaging with the focus group to improve natural disaster preparedness.

Outlines

00:00

🌐 Introduction to AI in Natural Disaster Management

Dr. Monique Goodluck, Innovation Manager at the Fraunhofer Institute of Telecommunication and Chair of the ITU-WMO Focus Group on AI for natural disaster management, discusses the application of AI in mitigating natural disaster risks. She explains that natural disasters, such as floods, droughts, and storms, have significant impacts on Africa, affecting food and nutrition security and causing economic losses. AI can help predict these disasters and forecast agricultural production by leveraging satellite data and machine learning, aiding in decision-making and adaptation planning for farmers and governments.

05:03

📊 Accessibility and Application of AI in African Agriculture

The conversation delves into the accessibility of data for AI applications, particularly in African agriculture. While much data is publicly available, it often requires processing and understanding to be useful. Dr. Goodluck emphasizes the need for accessible information on threats to all stakeholders in the food system. She acknowledges the diversity in African farms and the importance of tailored adaptation strategies. The focus group's work aims to explore how AI can provide hazard information and improve forecasting and communication of events to enable informed decision-making.

10:06

🌡️ AI's Role in Disaster Management Cycle

Dr. Goodluck outlines AI's potential at various stages of the disaster management cycle, focusing on preparedness and response. The discussion covers data collection and monitoring, modeling for forecasting events, and effective communication through early warning systems. High-quality data is essential for AI models, and the focus group is investigating how AI can enhance data quality, support real-time detection, and identify complex patterns. The goal is to improve natural disaster preparedness and response using AI and data-driven insights.

15:07

🌍 AI's Potential in Climate Change Adaptation in Africa

The podcast addresses how AI can help mitigate the risks associated with climate change in Africa, where hydrometeorological hazards are intensifying. AI can provide insights into hazard mechanisms, improve detection and forecasting, leading to more informed decisions on land development, crop selection, and evacuation strategies. Dr. Goodluck expresses hope that AI can reduce the costs of such events and benefit communities, especially in low-resource contexts.

20:10

🤝 Engaging Stakeholders in AI for Disaster Management

Dr. Goodluck invites engagement from various stakeholders, including governments, NGOs, and communities, in the focus group's efforts to utilize AI for disaster management. The group aims to create a roadmap, conduct workshops, and produce technical reports and educational materials. She stresses the importance of African expertise and data in building relevant AI models and encourages participation from the African community to ensure the focus group's work is impactful and context-specific.

Keywords

💡Artificial Intelligence (AI)

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, AI is discussed as a tool that can be applied to mitigate the risk of natural disasters. It is highlighted as a means to analyze data, predict events, and provide early warnings, which are crucial for disaster management. For instance, the script mentions AI's role in forecasting weather patterns and natural hazards, which can help in making informed decisions to reduce the impact of disasters.

💡Natural Disasters

Natural disasters are damaging physical events resulting from natural processes of the Earth, including atmospheric, hydrologic, geophysical, and biological phenomena. The video script discusses various types of natural disasters, emphasizing their impact on Africa, such as floods, droughts, and storms. These disasters are relevant to the discussion on how AI can be utilized to predict and manage their effects, thereby reducing associated risks and damages.

💡Climate Change

Climate change refers to long-term shifts in temperatures and weather patterns. It is mentioned in the script as a factor exacerbating the frequency and intensity of natural disasters, particularly in Africa. The discussion around climate change is significant as it sets the stage for exploring how AI can help in adapting to and mitigating the effects of these changes, such as predicting extreme weather events and planning for disaster response.

💡Remote Sensing

Remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. In the script, remote sensing is discussed in the context of using satellite data to monitor and predict natural disasters. This technology is crucial for AI applications in disaster management as it provides the data needed for analysis and forecasting, especially in areas that are logistically difficult to access.

💡Data Privacy

Data privacy concerns the right of individuals to have control over their personally identifiable information. The script touches upon data privacy issues related to the collection of data from sources like social media and satellite imagery. This is an important consideration in the application of AI, as it involves the handling of sensitive information that must be managed ethically and within legal frameworks to ensure confidentiality and trust.

💡Machine Learning

Machine learning is a subset of AI that provides systems the ability to learn and improve from experience without being explicitly programmed. The script refers to machine learning methods in the context of combining earth observations and weather forecasts to provide early warnings for natural disasters. Machine learning algorithms are essential for processing complex data sets and identifying patterns that can lead to accurate predictions.

💡Disaster Management Cycle

The disaster management cycle is a framework that encompasses the stages of disaster management, including preparedness, response, recovery, and mitigation. The script discusses the role of AI at various stages of this cycle, highlighting its potential to enhance data collection, monitoring, modeling, and effective communication during disaster preparedness and response. Understanding this cycle is key to appreciating how AI can be integrated into disaster management strategies.

💡Early Warning Systems

Early warning systems are mechanisms put in place to provide advanced notice of potential disasters. In the script, early warning systems are mentioned as a way AI can assist in disaster management by sending push messages to phones or using automated translation services. These systems are crucial for timely response and can save lives by alerting communities to imminent threats.

💡Agricultural Production

Agricultural production refers to the process of cultivating crops and livestock to produce food and other products. The script discusses how natural disasters can threaten crop productivity and disrupt supply chains, leading to food insecurity. AI's potential to predict weather patterns and natural disasters is highlighted as a means to support farmers in making informed decisions about farming practices and to help governments plan targeted relief efforts.

💡Focus Group

A focus group is a research technique used to gather opinions and feedback from a group of individuals on a particular topic. In the script, the focus group on AI for natural disaster management is discussed as a platform for collaboration among stakeholders, experts, and policymakers. The focus group's aim is to explore the potential and challenges of using AI in disaster management, with a particular interest in the African context.

Highlights

Dr. Monique Goodluck discusses the potential of AI in mitigating natural disaster risks.

Natural disasters are defined as damaging physical events of predominantly natural origin.

In Africa, epidemics, floods, droughts, and storms are the most impactful natural disasters.

AI can leverage satellite data to provide insights into natural disaster mechanisms and forecasting.

NASA's Harvest program combines earth observations and machine learning to provide early warnings for farmers.

AI can help in making informed decisions and adaptation plans to reduce the impact of natural disasters on agriculture.

Data from satellites and other sources are publicly available but may require processing for practical use.

Dr. Goodluck emphasizes the need for accessible and understandable data for all stakeholders in the food system.

AI can provide information about natural disaster mechanisms and improve forecasting and communication.

Natural disasters can have both direct and indirect impacts on health, including exposure to elements and infrastructure collapse.

AI and big data can intervene at each stage of the disaster management cycle, from preparedness to response.

The focus group on AI for natural disaster management aims to build a roadmap and engage researchers and stakeholders.

Dr. Goodluck highlights the importance of engaging African communities and stakeholders in AI research for natural disaster management.

AI has the potential to reduce the costs associated with natural disasters by providing better predictions and insights.

The focus group is an open community for stakeholders and experts to work together on AI in natural disaster management.

Dr. Goodluck invites interested parties to join the focus group and contribute to the discussion on AI and natural disasters.

Data privacy and confidentiality are important considerations when collecting data from various sources, including social media.

Transcripts

hello everyone welcome to this podcast

brought to you by efi the african forum

of artificial

artificial intelligence for help my

guest today is dr monique

goodluck she's the innovation manager

at friend offer institute of

telecommunication berlin

germany and chair of the newly

established

itu wmo

focus group on ai for natural disaster

management

he was the lead technical editor for

seven journals at the american

meteorological society

and has conducted research on

past climate change extreme weather

events and regional climate model

project

projections here today to talk about

how ai can be applied to mitigate the

risk of natural disaster

dr kudlish good afternoon hello good

afternoon thank you for the introduction

can you tell us what other natural

disasters how can we define it

certainly so natural disasters are

damaging physical events of a

predominantly natural origin

so they can be atmospheric hydrologic

geophysical

oceanographic biologic of these types of

origins

impacts can include everything from

injury to mortality

displacements damage to property or

cultural heritage

infrastructure and also disturbance to

nature natural resources

okay very good now all those natural

disasters which one

are more relevant to africa

yeah so i mean obviously africa is a big

continent so depending on where you are

there are going to be different types of

of hazards that are are more relevant

but um i think on a continental scale

in terms of impacts and mortality

epidemics

floods droughts and storms are are the

most

impactful okay

now it's worth mentioning that in africa

food and nutrition security are affected

by climate

and environmental assets the loss of

agricultural production is estimated

between

two and four percent gdp uh how do you

think ai

can help african farmers uh better

predict

uh natural disasters and and better

forecast

uh agricultural production yeah

um i mean indeed natural disasters

whether

droughts floods or the locust plagues

that we saw last summer in eastern

africa

can absolutely threaten crop

productivity and

disrupt supply chains which can cause

acute food insecurity

through ai we can leverage both ground

and remotely sensed

so satellite data to get insight into

the mechanisms of these hazards

as well as our ability to forecast and

warn for such disasters

so there are projects like nasa's

harvest program which works with

within this space combining earth

observations

weather forecasts with machine learning

methods

and providing early warnings in

particular for smallholder farmers in

africa

and this type of information can help us

make more informed decisions and

adaptation plans

so things like uh you know what are the

best farming practices that i can do

to reduce the impacts of such hazards on

the productivity in

in my farm or how can governments

mitigate the impacts of such hazards

using targeted relief

so this is something that uganda's

disaster risk financing fund has done

using early warning systems so that they

can really focus their relief

in in a targeted way there are some

challenges though as you can imagine um

we need to make sure that this

information about these threats

is accessible to those who are involved

in the food system

so decision makers farmers traders

everyone within this chain needs to have

access to this information

so that this acute food insecurity

doesn't

become chronic we also need to consider

that

particularly in sub-saharan africa there

are many small and diverse farms

each one has their own array of crops

and own

agricultural practices so adapting

to this type of event is going to vary

from farm to farm

so within the context of the focus group

we're exploring how ai can provide

information

about the mechanisms of these hazards

and how we can improve our ability to

forecast and communicate this type of

event

so that individuals in this situation

are able to use that information to make

decisions

interesting now since you're speaking

about uh data

i mean is it accessible uh is it easy to

get uh data let's say from nasa

or do you or do farmers need to go

through

some kind of bureaucracy

just to get the data they need to uh

you know that they're going to need to

forecast their production

yeah no it's a great question so a lot

of these data are publicly available

um they're maybe not so user-friendly

because they're gonna have imagery

you need to calibrate them you need to

know what you're looking for

um so i don't know um

how much someone needs to be aware of

these aspects of the data in order to

leverage them for this um

but you know obviously researchers who

are in this space are familiar with

these practices

so um they are of course able to access

the data and use them

okay so basically you need to kind of

process the data before you can actually

use it

yeah exactly you need to understand the

metadata you need to understand you know

the best way to use this data you need

to understand the sort of limitations of

the data

what to look for when it comes to data

quality so that it can be best used

okay i see um

now since you've been doing research

with some of the african researchers um

did you notice any um

difficulties uh when it came to africa

as far as using the data uh does africa

isn't kind of platform

or any kind of infrastructure i would

say

or ecosystem where we can actually use

the data

to apply to agriculture

or any other sectors that might need

data when it comes to ai

or for natural disasters so to be honest

i'm not

that far in the focus group to answer

that question it's a great question and

i look forward to exploring that

um so we've just kicked off the focus

group we're just trying to

engage researchers and stakeholders from

africa and our activities

um so i can't yet comment on that but

it's a good question i'm of course

curious to know if there are

you know platforms with benchmarking

data in africa and and you know how we

can access those so that's definitely

something that i'll be looking into

okay and as far as data collection on

the field in africa

um i know previously you mentioned you

know we need

weather uh stations uh different places

uh obviously that's the important data

that we need uh

what does my need might be needed on the

ground to make

ai work for natural disasters

yeah so i mean there's so many different

types of data that could be

integrated into an ai model um

and often there are times types of data

that you wouldn't even know would have

value but you

test it out and you find out that there

is some sort of a synergy or some sort

of relationship

so um i mean satellite data

are definitely a very important source

of information especially in this type

of

activity because you can access data

that

logistically would be difficult to get

otherwise also

data from drones so aerial imagery

from the ground you know instrumental

data whether

you know gauges and rivers or weather

stations

um are of course interesting and then of

course crowdsourced data can can be used

okay as far as the impact on health

how does natural disaster impact health

yeah that's a great question um it

definitely depends on the type of event

and vulnerability um but you can have

both

direct impacts and indirect impacts so

direct impacts um would be things

like through exposure to the elements a

heat stroke during a heat wave or

you know asphyxiation from forest fires

you know this would be

something that directly causes injury or

death um

also collapse of infrastructure you know

we saw with the

tsunami um and fukushima you can have

secondary disasters as well

um you can be struck by debris or

involved in traffic accidents these are

also sort of the what i would say are

direct impacts and then you also have

these

indirect impacts um contamination to

water supplies can spread

you know waterborne illness um pathogens

can be transported we've seen that

um with dust storms carrying fungi that

cause valley fever

um power outages we just had this in the

u.s

with the cold snap in you know texas

that you know people

you know suffered because they weren't

able to get heating um

of course you know food shortages other

disruptions to the supply chain

can can cause um consequences

or damage to hospitals

communications disruptions these can all

have indirect impacts on health

i see yeah there's a lot to consider

there i know

um now as as we know that disaster

management

management involves uh several stages uh

can you explain how

ai and big data intervene at each stage

of disaster management

so yeah so i mean as you say you know

there's the disaster management cycle

and of course

at all phases of the cycle there are

ways that ai can benefit

um i'm gonna sort of bring it back to

the focus group um

within the focus group we're looking at

a slice of the disaster management cycle

which is in the area of preparedness and

response so

we're looking at uh the potential and

also pitfalls

of using ai when it comes to data

collection

monitoring data handling so that's like

the first pillar

the second pillar is modeling so

forecasting events or also

reconstructing events

and then effective communication so

early warning systems this type of

communication um so i mean as

you know you and i know high quality

data are really the foundation for

ai models they're also the foundation

for understanding hazards and

the underlying mechanisms um they're

needed when it comes to providing ground

truth so understanding what you're

seeing in satellite image

calibrating models and and building

these

reliable ai algorithms so things in the

focus group that we're going to ask

in this space are how can ai be used to

enhance

data quality and data quantity how can

ai support

the detection of features in real time

so if you have a seismic time series how

can we

detect you know in real time that an

event is happening

and how can we use it to identify

complex relationships

and patterns within data

when it comes to modeling we're looking

at um how

these algorithms can enhance and improve

traditional models

so physics based or numerical based

models

and some questions that we could look at

are what's the current gold standard

method when it comes

to making a forecast or a reconstruction

and how can these algorithms

bring that to the next level what

requirements should data meet when we're

training and testing

this type of algorithm and what should

we consider when we evaluate an

algorithm

what metrics should we use what

expectations should we have when it

comes to explainability or transparency

and then within effective communication

so this would be this sort of response

part of the um cycle

we might consider once a disaster has

been forecast or triggered how can ai

assist with

creating an early warning with sending a

push message to a phone or

using automated translation services how

do we ensure that communications methods

are reliable and trusted by the

population

are they accompanied by protocols to

ensure that individuals know how to

respond to these messages

so again this is just a slice of the

disaster management cycle of course

ai could be used in other areas but this

is where we're focusing for the focus

group

okay

all right um

in africa the effect of climate change

as uh

that's where exacerbated the frequency

and intensity of floods

in some areas uh like mozambique a

couple years ago they had a huge flood

there

uh water everywhere a lot of people got

displaced

um and then the northern part of africa

is

impacted by droughts you know countries

like um china and

ethiopia um and drc and kenya had issues

with

heavy rain and and and flood lands

slides as well

with all those natural disasters

happening due to

climate change and global warming uh

how do you see ai uh mitigating

the risk of associating the freight of

climate change

uh in this part of the world yeah

so i mean unfortunately such

hydrometeorological hazards

have been intensifying and will probably

continue to intensify

um in many regions of the world as a

result of climate change

um it's also related to you know where

we're settling where

our urbanization is happening um and and

the way these factors interact

um i mean through giving insight into

the mechanisms behind these hazards

helping us detect

disasters and giving more accurate

forecasts

ai is going to let us make more informed

decisions

so where should i consider to develop

land what crops should i

choose to use because they're more

resistant to this type of event

um when an event is happening you know

how

and where and when should we evacuate

and what's the best way to do so

so it's my hope that through having this

extra insight we can

reduce the costs of such events

there's definitely a cost benefit

associated with ai

now for the low context of africa low

resource context

obviously africa need technology

that's pretty much known how do you

think we can maybe

vulgarize ai to where it's going to be

accessible

to smaller communities like villages

especially when it comes to disaster

management

what kind of model are you saying

that we can we can use in africa to

disseminate ai

to where even you know the smaller

communities that already have high tech

uh can benefit from ai and and prevent

natural disasters in their

smaller communities yeah what can be

done

in that area you think yeah it's a good

question so i mean i think on the one

hand there's the question

i mean when it comes to you know

building the models

so obviously you know there are such

great research happening in africa

of course to build models you need to

have a certain amount of computing

capability and capacity so that's

obviously

you know something that needs to be

considered uh you know these types of

resources

so that the research can be done within

africa using african data and

african expertise um when it comes to

actually using the outcome of these

this research so the model um i mean

we've seen from for example from the

focus group on ai for health

you know with a smartphone you can do

incredible things

you know detecting

you know skin lesions

using your phone uh identifying snakes

species um in the field so i think that

um

we shouldn't underestimate what a

smartphone can do because those are

uh quite available to

i would assume also people who are you

know

using farms um so if they you know have

a pest or or something else that they

want to identify i would imagine that

this would be something that they could

use

very good um what about the government

and ngos obviously they have more

resources than smaller communities

what can they do to get more involved in

ai

and and and better prepare for natural

disasters uh in the african context

you think yeah i mean that's a great

question so

um our research in the focus group

we're gonna be working on four kinds of

deliverables

um we're going to be building a road map

of ai activities

within this area of natural disaster

management

we're going to be having workshops that

are going to be bringing together

experts and stakeholders

and highlighting activities within this

framework

and i should mention that our kickoff

workshop is going to be this monday

from 10 a.m to 2 p.m west africa time

and you can register on our website um

and then we're also gonna have meetings

the following two days tuesday and

wednesday

um we're also going to be making uh

technical reports to summarize the key

findings of our analyses based on

selected use cases and educational

materials to support capacity building

now for these deliverables to have value

for african stakeholders it's really

important that we have engagement from

the region i want to know

what use cases are most relevant for the

african community i want to know

what research is being done in the

african community i want that to be in

our

analysis and in our report um i know

that there's really exciting work being

done

on the continent um in the past few

weeks i've had the pleasure of speaking

with

representatives from the disaster risk

reduction group at the african union um

with the vice chair from the african

science and technology advisory group

with volcanologists from the goma

volcano laboratory and drc

and researchers from strathmore

university in kenya and of course i also

know through

the other focus group on ai for health

many researchers in africa that are

doing amazing research in this space

so this has all shown me that

you know africa is a very fertile ground

when it comes to using ai

so you know there's no reason that we

shouldn't have really active engagement

within this focus group

we have abundance of data we have top

tier machine learning experts

and there's an interest in the

application so i think that that's going

to be really key when it comes to

bringing in

the communities governments the ngos

into the focus group so that they can

improve

natural disaster preparedness okay

very good dr kuvlich

thank you pretty much answer all our

questions i don't know if you want to

add

anything uh that you think might be

relevant for this podcast

um as far as ai and how it can you know

help prevent natural disasters maybe

that we need to cover

um well i i would just love to sort of

loop back to the focus group

and and just you know encourage anyone

who's interested in ai

who's interested in in data who's

interested in

how this can be used to support natural

disaster management anyone from the

natural disaster or drr

space the focus group is really an open

community

it's a platform for us all to work

together on this topic

whether your expertise is on the natural

disaster spectrum

disastrous reduction spectrum or the ai

spectrum

this is really a space for stakeholders

experts

policy makers researchers everybody to

get together

and to to tackle this head on so i

really

hope that anyone with an interest in the

topic will

visit the focus group website i don't

know if i can

put it in the chat maybe that's the

easiest

i actually have a few questions from the

the chat so that

i'm gonna ask just came in stick it in

the chat so that anyone can see it and

yes

please there we go uh

i have a question about the collection

of data um

what kind of issues are you seeing

with the collection of data when it

comes to trying to get the data from

social media do you see any kind of

confidentiality

issues uh with that or is it pretty much

um

you know open for everybody to get get

whatever data they want to get

from platform like um

facebook or something else uh

is it pretty easy to get data from those

uh platforms or not

that's a really great question so like i

said we're sort of at this

nascent stage of the focus group so we

haven't even gotten to that level yet

of collecting data we're just building

up a community

and this is definitely something that

we're going to look into i mean also

when it comes to

you know imagery from from drones or or

imagery from satellites i mean there are

also

you know data privacy issues that we

need to keep into consideration

um so this is definitely something

that's on my mind and it's definitely a

great question but

within the the focus group we're not at

that level yet where we were addressing

that but it will be something that we

look into

okay all right sounds good i don't have

any other questions for you

um thanks a lot for your time uh this

was

very good a great podcast

i wish you all the luck with uh your fox

group thank you very much thank you and

uh for the rest of the audience uh this

podcast is gonna be archived

on uh the african form of artificial

artificial intelligence

website the address is www.afaih.com

thank you very much take care bye-bye